Seeing Through the Eyes of Machines The method of observing the surrounding environment through machines such as computers or mobile phones is called computer vision. The rigorous work of simulating the human eye dates back to the 1950s, and we have come a long way in this field. Computer vision has entered our smartphones through … Read more
Introduction The most common application of the Hessian matrix is in the Newton optimization method, which mainly seeks the extremum points of a function where the first derivative is zero. This article provides a straightforward summary of the two applications of the Hessian matrix in the XGBoost algorithm, namely the minimum child weight algorithm and … Read more
Every algorithm has its applicable range, and understanding its pros and cons can help avoid errors caused by inappropriate use. This article summarizes the pros and cons of common machine learning algorithms for reference. The sources are from “Machine Learning: Using R, Tidyverse, and mlr” (Algorithms 1 to 17) and “Neural Networks: Implementation in R” … Read more
Click on the above“Beginner’s Guide to Vision” to choose to add a Star Mark or “Top” Important content delivered promptly Introduction The most common application of the Hessian matrix is in the Newton method optimization algorithm, which primarily seeks the extrema of a function where the first derivative is zero. This article provides a clear … Read more
When I first started exploring machine learning algorithms, I was overwhelmed by all the mathematical content. I found that without fully understanding the intuition behind the algorithm, it was difficult to grasp the underlying mathematical principles. Therefore, I tend to favor explanations that break down the algorithm into simpler, more digestible steps. This is what … Read more
XGBoost utilizes a second technique which is second-order optimization, expanding the loss function l(x,y) using a Taylor series expansion. To approximate it to the second order. This is relatively unique in the XGBoost algorithm, differing from the approach of optimizing using gradient descent in GBT, and also different from Adaboost which increases the weights of … Read more
Introduction The previous article introduced the algorithm principles of XGBoost and introduced the scoring function (objective function) that measures the quality of tree structures. The best split point is selected based on the scoring function before and after the feature split points, but a detailed introduction to the node splitting algorithm was not provided. This … Read more
Smart Introduction In recent years, Jiangsu courts have deeply implemented Xi Jinping’s thoughts on the rule of law and his important ideas on building a strong networked nation, closely focusing on the work theme of “justice and efficiency.” They have actively explored the deep integration of artificial intelligence and judicial applications, relying on multimodal perception … Read more
Click on the “Visual Learning for Beginners” above, choose to add “Star” or “Pin“. Essential Knowledge Delivered Instantly This article is sourced from the AI Knowledge Base and reprinted from Smart Vehicle Technology. The article is for academic exchange only. / Introduction/ Object tracking is an important problem in the field of computer vision, currently … Read more
Author: Huang Chongyuan “Data Insect Nest” Total 23138 words Cover image ssyer.com “ In recommendation systems or computational advertising, ReRank blatantly disrupts the sequence generated by recall, coarse ranking, and fine ranking, yet claims to act in the greater interest. This is a very interesting phase of the algorithm, full of fun and, of course, … Read more